用生成模型分离星系光度参数，高效解耦形态特征。
A Generative Model for Disentangling Galaxy Photometric Parameters
- 基于条件自编码器，从星系图像中解耦关键参数
- 在真实模拟数据上实现高精度形态参数重建
- 适合大规模星系巡天的数据分析与自动化处理
当前及未来的光度巡天将产生前所未有的海量星系图像，亟需高效可靠的手段大规模提取星系形态参数。传统参数化光分布拟合方法虽具价值，但在处理数十亿源时计算成本过高。本文提出一种条件自编码器（CAE）框架，同时建模和表征星系形态。该模型在基于GalSim生成的大量真实模拟星系图像上训练，覆盖多种星系类型、光度参数（如通量、半光半径、Sersic指数、扁率）及观测条件。通过将每幅星系图像编码为低维潜在表示，并以关键参数条件化，模型能以解耦方式有效恢复这些形态特征，同时重构原始图像。结果表明，该方法可准确高效地推断复杂结构属性，为现有方法提供有力替代方案。
原文摘要 · Abstract (English)
Ongoing and future photometric surveys will produce unprecedented volumes of galaxy images, necessitating robust, efficient methods for deriving galaxy morphological parameters at scale. Traditional approaches, such as parametric light-profile fitting, offer valuable insights but become computationally prohibitive when applied to billions of sources. In this work, we propose a Conditional AutoEncoder (CAE) framework to simultaneously model and characterize galaxy morphology. Our CAE is trained on a suite of realistic mock galaxy images generated via GalSim, encompassing a broad range of galaxy types, photometric parameters (e.g., flux, half-light radius, Sersic index, ellipticity), and observational conditions. By encoding each galaxy image into a low-dimensional latent representation conditioned on key parameters, our model effectively recovers these morphological features in a disentangled manner, while also reconstructing the original image. The results demonstrate that the CAE approach can accurately and efficiently infer complex structural properties, offering a powerful alternative to existing methods.
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